Tutorial Proposal

Leaflets

Establishing Good Benchmarks and Baselines in Artificial and Biological Vision

Abstract

Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain's anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, ostensibly "natural" images have become popular in the study of vision, and have been used to show apparently impressive progress in building such models. In this talk, we will demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially hindering progress and guiding the community in the wrong directions. Instead, we re-examine what it means for images to be natural and argue for a renewed focus on the core problem of object recognition -- real-world image variation.

Short Bio

Nicolas Pinto is currently Chief Scientist and Chief Technology Officer of two Silicon Valley stealth startups, focusing on the research and development of human-level brain-inspired perception technologies and their real-time applications on low-power embedded devices. He holds two M.S. in Computer Science and Engineering from France (UTBM/ENSISA, 2007), and a Ph.D. in Neuroscience from the USA (MIT, 2010) supported by NSF, DARPA, NVIDIA, Google, Amazon and Microsoft. Previously he was a graduate-level Lecturer in Computer Science at Harvard SEAS/DCE teaching Massively Parallel Computing, and a Research Scientist in Prof. Jim DiCarlo's Lab at MIT and Prof. David Cox's Lab at Harvard developing large-scale computational models of the visual cortex.